Improving the productivity of a sales force is a challenge. By identifying patterns of sales successes and failures, sales representatives can work to replicate or avoid similar efforts and thereby avoid wasting cycles of cold-calling and uneventful sales activities. Sales patterns can be observed and identified. These patterns offer a window into future buying behavior.
But sales patterns are not readily identifiable to the sale and marketing organization, and therefore it is difficult for the organization to take action to improve sales success. Typical multidimensional analysis of the various factors impacting purchasing by industry, geography, customer size, install base, and the like are often too time-consuming for the sales and marketing groups to undertake.
Sales predictor engines (SPE) may be used to create rules and models that will help predict and recommend potential sales based on customer attributes. SPE software will typically support common attributes that sales analysts may use to create rules and models for sales prediction. However, it is often desirable to include additional attributes that may not be supported by the SPE out of the box. For example, a sales analyst selling in China may wish to implement a “province” attribute in order to create rules and sales prediction models for Chinese customers, whereas such an attribute would not be needed for other customers, such as those in the United States.
Therefore, a need exists for being able to implement extended attributes and entities for a sales predictor engine.